Effects of deformable registration algorithms on the creation of statistical maps for preoperative targeting in deep brain stimulation procedures

Yuan Liu; Pierre-Francois D'Haese; Benoit M. Dawant

doi:10.1117/12.2043529

12 March 2014 Effects of deformable registration algorithms on the creation of statistical maps for preoperative targeting in deep brain stimulation procedures

Yuan Liu, Pierre-Francois D'Haese, Benoit M. Dawant

Author Affiliations +

Proceedings Volume 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling; 90362B (2014) https://doi.org/10.1117/12.2043529
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Deep brain stimulation, which is used to treat various neurological disorders, involves implanting a permanent electrode into precise targets deep in the brain. Accurate pre-operative localization of the targets on pre-operative MRI sequence is challenging as these are typically located in homogenous regions with poor contrast. Population-based statistical atlases can assist with this process. Such atlases are created by acquiring the location of efficacious regions from numerous subjects and projecting them onto a common reference image volume using some normalization method. In previous work, we presented results concluding that non-rigid registration provided the best result for such normalization. However, this process could be biased by the choice of the reference image and/or registration approach. In this paper, we have qualitatively and quantitatively compared the performance of six recognized deformable registration methods at normalizing such data in poor contrasted regions onto three different reference volumes using a unique set of data from 100 patients. We study various metrics designed to measure the centroid, spread, and shape of the normalized data. This study leads to a total of 1800 deformable registrations and results show that statistical atlases constructed using different deformable registration methods share comparable centroids and spreads with marginal differences in their shape. Among the six methods being studied, Diffeomorphic Demons produces the largest spreads and centroids that are the furthest apart from the others in general. Among the three atlases, one atlas consistently outperforms the other two with smaller spreads for each algorithm. However, none of the differences in the spreads were found to be statistically significant, across different algorithms or across different atlases.

Citation Download Citation

Yuan Liu, Pierre-Francois D'Haese, and Benoit M. Dawant "Effects of deformable registration algorithms on the creation of statistical maps for preoperative targeting in deep brain stimulation procedures", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90362B (12 March 2014); https://doi.org/10.1117/12.2043529

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